A Control Parameters Design Method With Multi-Constrains for an LCL-Filtered Grid-Connected Inverter in a Weak Grid

Under weak grid conditions, the variation of the grid impedance will affect the steady-state and dynamic performance of the LCL-filtered grid-connected inverter and even make the inverter unstable. To ensure the system stability and further improve the dynamic performance in a weak grid, a control parameter design method with multi-constrains considering the system bandwidth for the current controller and active damping is proposed in this paper. First, based on the current controller and active damping with only grid current feedback, the effects of control parameters and grid impedance on the LCL resonant suppression and the performance of the inverter are analyzed. Moreover, the parameter constraints of the controllers are derived considering the grid impedance, including stability, resonance suppression, and margin constraints. Furthermore, as the system bandwidth affects the dynamic performance of the inverter, combined with the obtained multi-constraints, the optimal control parameters are determined by achieving the maximum bandwidth of the system against the impedance variation. Compared with other two methods, when the proposed method is applied, the system can operate with a better dynamic and steady-state performance. Finally, experiments are performed on a 2 kW three-phase grid-connected inverter in the weak grid, which verify the effectiveness of the parameter design method proposed in this paper.

Parameter Design for Group Method Data Handling (GMDH) using Taguchi in Software Effort Estimation

Recently, the use of data-driven models is becoming increasingly impactful but has proven to offer best prediction with less knowledge of the geological, hydrological, and physical process behaviour and criteria. A Group Data Handling Model (GMDH) is one of the sub-model common neural network data driven. It was first developed for complex systems with a modelling and recognition algorithm. GMDH is known as a self-organizing heuristic modelling approach. For solving modelling issues involving multiple inputs to single output data, it is very successful. While the GMDH model has been implemented in many modelling fields, some modifications in terms of parameter design have been given little attention. In other respects, Dr. Genichi Taguchi suggested that the Taguchi method for improving the process or product design with the help of significant parameter levels that influence the delivery of the product. In this paper, we evaluated the behaviour of GMDH model based on numbers of neuron per layer, hidden layer, alpha, and train ratio parameters using Taguchi method. Cocomo and Kemerer datasets are used to test our hypothesized scenarios. The result shows that number of neurons, layer and train ratio are the important parameters that affects the performance of the GMDH model.

The Method for Optimum Design of Resin Injection Molding Process Conditions using Multivariate Analysis with Robust Parameter Design

In recent years, the demand for plastic products has increased, and along with the deepening of academics, mass production, weight reduction, and high precision are progressing. In the fields of design development and production technology, there are many issues related to quality assurance such as molding defects and product strength. In particular, in the resin molding process, there is a high degree of freedom in product shape and mold structure, and it is an important issue to create quality functions that apply analysis of complex multidimensional information. In this study, the important factors of the resin molding process related to the optimization of resin strength are extracted by applying the multivariate analysis method and robust parameter design. As a result of verification of the proposed method, it is clarified that uniformization of the resin filling density in the mold is extremely important for stabilizing the resin strength.

Robust Parameter Design of Shielded Metal Arc Welding (SMAW) for Optimum Tensile Strength

Shielded Metal Arc Welding (SMAW), an arc welding process, is widely used in applications. In practice, SMAW is widely applied to the welding process on hollow square pipe. Performance expected from this welding is the tensile strength of weld joint. The tensile strength is influenced by parameters process which have possibility for an optimization process to become ‘robust’. Robust is a design which less sensitive to the effect of uncertain quantities or noise factors. Taguchi method is the most efficient optimization method which accommodates the noise factors effect and requires less experiment. This study is focusing on optimizing the welding process on hollow square pipe. Parameters process such as welding current (I), electrode angle (θ), root gap (d) and electrode type (E) are adopted as parameters design. Taguchi method are chosen as a strategy and L9 fractional orthogonal array are chosen as the design experiment, which only 9 experiment samples needed from 81 experiments that should have been carried out for full factorial design. The objectivity is to maximize the tensile strength of weld joint. Three replications of L9 fractional orthogonal array Taguchi had been performed to generate the tensile strength and estimates the fluctuation of the output caused by noise factors. This study found that the welding current of 100A (I), electrode angle (θ) of 90°, root gap (d) of 2 mm, and electrode type (E) of E7018 produce the optimum results. Tensile strength improved from this robust parameter design is about 98.39 MPa based on initial parameter design.

A Novel Detection Performance Modular Evaluation Metric of Space-based Infrared System

In order to reflect the space-based full chain information of the detection process comprehensively and objectively, we proposed a novel modular evaluation metric to discuss the target, background and system independently. It takes the equivalent radiation intensity as the parameter, which can evaluate the detection performance of the system quantitatively. In this paper, taking the fifth-generation American stealth fighter F22 as an example, the mathematical detection model of the space-based infrared system to aircraft targets in the Earth background is described. A modular evaluation metric is proposed. The simulation analyzes the impact of different detection scenes and system parameters on system equivalent irradiance. Furthermore, recommendations for the optimization of the detection system are given. The research results provide a new idea for the analysis of the detection performance of highly maneuverable targets under dynamic backgrounds and have guiding significance for the performance evaluation and parameter design of the infrared detection system.

Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks

Background The prevalence of chronic disease is growing in aging societies, and artificial-intelligence–assisted interpretation of macular degeneration images is a topic that merits research. This study proposes a residual neural network (ResNet) model constructed using uniform design. The ResNet model is an artificial intelligence model that classifies macular degeneration images and can assist medical professionals in related tests and classification tasks, enhance confidence in making diagnoses, and reassure patients. However, the various hyperparameters in a ResNet lead to the problem of hyperparameter optimization in the model. This study employed uniform design—a systematic, scientific experimental design—to optimize the hyperparameters of the ResNet and establish a ResNet with optimal robustness. Results An open dataset of macular degeneration images (https://data.mendeley.com/datasets/rscbjbr9sj/3) was divided into training, validation, and test datasets. According to accuracy, false negative rate, and signal-to-noise ratio, this study used uniform design to determine the optimal combination of ResNet hyperparameters. The ResNet model was tested and the results compared with results obtained in a previous study using the same dataset. The ResNet model achieved higher optimal accuracy (0.9907), higher mean accuracy (0.9848), and a lower mean false negative rate (0.015) than did the model previously reported. The optimal ResNet hyperparameter combination identified using the uniform design method exhibited excellent performance. Conclusion The high stability of the ResNet model established using uniform design is attributable to the study’s strict focus on achieving both high accuracy and low standard deviation. This study optimized the hyperparameters of the ResNet model by using uniform design because the design features uniform distribution of experimental points and facilitates effective determination of the representative parameter combination, reducing the time required for parameter design and fulfilling the requirements of a systematic parameter design process.